Sci Rep. 2023 Nov 8;13(1):19358. doi: 10.1038/s41598-023-46648-1.

ABSTRACT

In the field of rare and intractable diseases, new drug development is difficult and drug repositioning (DR) is a key method to improve this situation. In this study, we present a new method for finding DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM). At first, we used a spleen associated tyrosine kinase inhibitor as a reference to evaluate the technique, and succeeded in scaffold-hopping maintaining the pharmacological activity. Then we applied this method to five drugs and obtained 144 compounds with diverse structures. Among these, 31 compounds were known to target the same proteins as their reference compounds and 113 compounds were known to target different proteins. We found that AI-AAM dominantly selected functionally similar compounds; thus, these selected compounds may represent improved alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of their references as well. This new "compound-target" information provided DR candidates that could be utilized for future drug development.

PMID:37938624 | DOI:10.1038/s41598-023-46648-1

Antiviral Res. 2023 Nov 5:105740. doi: 10.1016/j.antiviral.2023.105740. Online ahead of print.

ABSTRACT

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

PMID:37935248 | DOI:10.1016/j.antiviral.2023.105740

Nucleic Acids Res. 2023 Nov 2:gkad1004. doi: 10.1093/nar/gkad1004. Online ahead of print.

ABSTRACT

ChEMBL (https://www.ebi.ac.uk/chembl/) is a manually curated, high-quality, large-scale, open, FAIR and Global Core Biodata Resource of bioactive molecules with drug-like properties, previously described in the 2012, 2014, 2017 and 2019 Nucleic Acids Research Database Issues. Since its introduction in 2009, ChEMBL's content has changed dramatically in size and diversity of data types. Through incorporation of multiple new datasets from depositors since the 2019 update, ChEMBL now contains slightly more bioactivity data from deposited data vs data extracted from literature. In collaboration with the EUbOPEN consortium, chemical probe data is now regularly deposited into ChEMBL. Release 27 made curated data available for compounds screened for potential anti-SARS-CoV-2 activity from several large-scale drug repurposing screens. In addition, new patent bioactivity data have been added to the latest ChEMBL releases, and various new features have been incorporated, including a Natural Product likeness score, updated flags for Natural Products, a new flag for Chemical Probes, and the initial annotation of the action type for ∼270 000 bioactivity measurements.

PMID:37933841 | DOI:10.1093/nar/gkad1004

Bioinformatics. 2023 Oct 31:btad666. doi: 10.1093/bioinformatics/btad666. Online ahead of print.

ABSTRACT

MOTIVATION: Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network which contains information of various biological interactions could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA).

RESULTS: We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments.

AVAILABILITY: Source code is available at https://github.com/XinmengLiao/Open_MoA.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37930015 | DOI:10.1093/bioinformatics/btad666

Cancer Biol Med. 2023 Nov 6:j.issn.2095-3941.2023.0281. doi: 10.20892/j.issn.2095-3941.2023.0281. Online ahead of print.

ABSTRACT

Cancer remains a significant global health challenge with limited treatment options beyond systemic therapies, such as chemotherapy, radiotherapy, and molecular targeted therapy. Immunotherapy has emerged as a promising therapeutic modality but the efficacy has plateaued, which therefore provides limited benefits to patients with cancer. Identification of more effective approaches to improve patient outcomes and extend survival are urgently needed. Drug repurposing has emerged as an attractive strategy for drug development and has recently garnered considerable interest. This review comprehensively analyses the efficacy of various repurposed drugs, such as transforming growth factor-beta (TGF-β) inhibitors, metformin, receptor activator of nuclear factor-κB ligand (RANKL) inhibitors, granulocyte macrophage colony-stimulating factor (GM-CSF), thymosin α1 (Tα1), aspirin, and bisphosphonate, in tumorigenesis with a specific focus on their impact on tumor immunology and immunotherapy. Additionally, we present a concise overview of the current preclinical and clinical studies investigating the potential therapeutic synergies achieved by combining these agents with immune checkpoint inhibitors.

PMID:37929901 | DOI:10.20892/j.issn.2095-3941.2023.0281

Indian J Pharmacol. 2023 Sep-Oct;55(5):322-331. doi: 10.4103/ijp.ijp_626_22.

ABSTRACT

Drug discovery has customarily focused on a de novo design approach, which is extremely expensive and takes several years to evolve before reaching the market. Discovering novel therapeutic benefits for the current drugs could contribute to new treatment alternatives for individuals with complex medical demands that are safe, inexpensive, and timely. In this consequence, when pharmaceutically yield and oncology drug efficacy appear to have hit a stalemate, drug repurposing is a fascinating method for improving cancer treatment. This review gathered about how in silico drug repurposing offers the opportunity to quickly increase the anticancer drug arsenal and, more importantly, overcome some of the limits of existing cancer therapies against both old and new therapeutic targets in oncology. The ancient nononcology compounds' innovative potential targets and important signaling pathways in cancer therapy are also discussed. This review also includes many plant-derived chemical compounds that have shown potential anticancer properties in recent years. Here, we have also tried to bring the spotlight on the new mechanisms to support clinical research, which may become increasingly essential in the future; at the same time, the unsolved or failed clinical trial study should be reinvestigated further based on the techniques and information provided. These encouraging findings, combined together, will through new insight on repurposing more non-oncology drugs for the treatment of cancer.

PMID:37929411 | DOI:10.4103/ijp.ijp_626_22

Biomed Pharmacother. 2023 Nov 3;168:115817. doi: 10.1016/j.biopha.2023.115817. Online ahead of print.

ABSTRACT

Metformin is a widespread antidiabetic agent that is commonly used as a treatment against type 2 diabetes mellitus patients. Regarding its therapeutic potential, multiple studies have concluded that Metformin exhibits antineoplastic activity on several types of cancer, including endometrial carcinoma. Although Metformin's antineoplastic activity is well documented, its cellular and molecular anticancer mechanisms are still a matter of controversy because a plethora of anticancer mechanisms have been proposed for different cancer cell types. In this study, we addressed the cellular and molecular mechanisms of Metformin's antineoplastic activity by using both in vitro and in vivo studies of Pten-loss driven carcinoma mouse models. In vivo, Metformin reduced endometrial neoplasia initiated by Pten-deficiency. Our in vitro studies using Pten-deficient endometrial organoids focused on both cellular and molecular levels in Metformin's tumor suppressive action. At cellular level, we showed that Metformin is involved in not only the proliferation of endometrial epithelial cells but also their regulation via a variety of mechanisms of epithelial-to-mesenchymal transition (EMT) as well as TGF-β-induced apoptosis. At the molecular level, Metformin was shown to affect the TGF-β signalling., a widely known signal that plays a pivotal role in endometrial carcinogenesis. In this respect, Metformin restored TGF-β-induced apoptosis of Pten-deficient endometrial organoids through a p38-dependent mechanism and inhibited TGF-β-induced EMT on no-polarized endometrial epithelial cells by inhibiting ERK/MAPK signalling. These results provide new insights into the link between the cellular and molecular mechanism for Metformin's antineoplastic activity in Pten-deficient endometrial cancers.

PMID:37925934 | DOI:10.1016/j.biopha.2023.115817

Artif Intell Med. 2023 Nov;145:102687. doi: 10.1016/j.artmed.2023.102687. Epub 2023 Oct 21.

ABSTRACT

Drug repurposing has gained the attention of many in the recent years. The practice of repurposing existing drugs for new therapeutic uses helps to simplify the drug discovery process, which in turn reduces the costs and risks that are associated with de novo development. Representing biomedical data in the form of a graph is a simple and effective method to depict the underlying structure of the information. Using deep neural networks in combination with this data represents a promising approach to address drug repurposing. This paper presents BEHOR a more comprehensive version of the REDIRECTION model, which was previously presented. Both versions utilize the DISNET biomedical graph as the primary source of information, providing the model with extensive and intricate data to tackle the drug repurposing challenge. This new version's results for the reported metrics in the RepoDB test are 0.9604 for AUROC and 0.9518 for AUPRC. Additionally, a discussion is provided regarding some of the novel predictions to demonstrate the reliability of the model. The authors believe that BEHOR holds promise for generating drug repurposing hypotheses and could greatly benefit the field.

PMID:37925215 | DOI:10.1016/j.artmed.2023.102687

Mol Biol Rep. 2023 Nov 4. doi: 10.1007/s11033-023-08868-6. Online ahead of print.

ABSTRACT

Triple-negative breast cancer (TNBC) is known for its heterogeneous complexity and is often difficult to treat. TNBC lacks the expression of major hormonal receptors like estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 and is further subdivided into androgen receptor (AR) positive and AR negative. In contrast, AR negative is also known as quadruple-negative breast cancer (QNBC). Compared to AR-positive TNBC, QNBC has a great scarcity of prognostic biomarkers and therapeutic targets. QNBC shows excessive cellular growth and proliferation of tumor cells due to increased expression of growth factors like EGF and various surface proteins. This study briefly reviews the limited data available as protein biomarkers that can be used as molecular targets in treating TNBC as well as QNBC. Targeted therapy and immune checkpoint inhibitors have recently changed cancer treatment. Many studies in medicinal chemistry continue to focus on the synthesis of novel compounds to discover new antiproliferative medicines capable of treating TNBC despite the abundance of treatments currently on the market. Drug repurposing is one of the therapeutic methods for TNBC that has been examined. Moreover, some additional micronutrients, nutraceuticals, and functional foods may be able to lower cancer risk or slow the spread of malignant diseases that have already been diagnosed with cancer. Finally, nanomedicines, or applications of nanotechnology in medicine, introduce nanoparticles with variable chemistry and architecture for the treatment of cancer. This review emphasizes the most recent research on nutraceuticals, medication repositioning, and novel therapeutic strategies for the treatment of TNBC.

PMID:37924450 | DOI:10.1007/s11033-023-08868-6

Cancer Lett. 2023 Nov 1:216469. doi: 10.1016/j.canlet.2023.216469. Online ahead of print.

ABSTRACT

Translational research and the development of targeted therapies have transformed the therapeutic landscape in epithelial ovarian cancer (EOC) over the last decade. However, recurrent ovarian cancer continues to pose formidable challenges to therapeutic interventions, necessitating innovative strategies to optimize treatment outcomes. Current research focuses on the development of pharmaceuticals that target potential resistance pathways to DNA repair pathways. However, the cost and toxicity of some of these therapies are prohibitive and majority of patients lack access to clinical trials. Metronomic chemotherapy, characterized by the continuous administration of low doses of chemotherapeutic agents without long treatment breaks, has emerged as a promising approach with potential implications beyond recurrent setting. It acts primarily by inhibition of angiogenesis and activation of host immune system. We here review the mechanism of action of metronomic chemotherapy, as well as its current role, limitations, and avenues for further research in the management of epithelial ovarian cancer.

PMID:37923056 | DOI:10.1016/j.canlet.2023.216469

IEEE J Biomed Health Inform. 2023 Nov 3;PP. doi: 10.1109/JBHI.2023.3328337. Online ahead of print.

ABSTRACT

Viral infections have emerged as significant public health concerns for decades. Antiviral drugs, specifically designed to combat these infections, have the potential to reduce the disease burden substantially. However, traditional drug development methods, based on biological experiments, are resource-intensive, time-consuming, and low efficiency. Therefore, computational approaches for identifying antiviral drugs can enhance drug development efficiency. In this study, we introduce AntiViralDL, a computational framework for predicting virus-drug associations using self-supervised learning. Initially, we construct a reliable virus-drug association dataset by integrating the existing Drugvirus2 database and FDA-approved virus-drug associations. Utilizing these two datasets, we create a virus-drug association bipartite graph and employ the Light Graph Convolutional Network (LightGCN) to learn embedding representations of viruses and drugs. To address the sparsity of virus-drug association pairs, AntiViralDL incorporates contrastive learning to improve prediction accuracy. We implement data augmentation by adding random noise to the embedding representation space of virus and drug nodes, as opposed to traditional edge and node dropout. Finally, we calculate an inner product to predict virus-drug association relationships. Experimental results reveal that AntiViralDL achieves AUC and AUPR values of 0.8450 and 0.8494, respectively, outperforming four benchmarked virus-drug association prediction models. The case study further highlights the efficacy of AntiViralDL in predicting anti-COVID-19 drug candidates.

PMID:37922162 | DOI:10.1109/JBHI.2023.3328337

Expert Opin Drug Discov. 2023 Nov 3:1-13. doi: 10.1080/17460441.2023.2276349. Online ahead of print.

ABSTRACT

INTRODUCTION: Toxoplasmosis constitutes a challenge for public health, animal production and welfare. Since more than 60 years, only a limited panel of drugs has been in use for clinical applications.

AREAS COVERED: Herein, the authors describe the methodology and the results of library screening approaches to identify inhibitors of Toxoplasma gondii and related strains. The authors then provide the reader with their expert perspectives for the future.

EXPERT OPINION: Various library screening projects, in particular those using reporter strains, have led to the identification of numerous compounds with good efficacy and specificity in vitro. However, only few compounds are effective in suitable animal models such as rodents. Whereas no novel compound has cleared the hurdle to applications in humans, the few compounds with known indication and application profiles in human patients are of interest for further investigations. Taken together, drug repurposing as well as high-throughput screening of novel compound libraries may shorten the way to novel drugs against toxoplasmosis.

PMID:37921660 | DOI:10.1080/17460441.2023.2276349

Drug Dev Res. 2023 Nov 3. doi: 10.1002/ddr.22125. Online ahead of print.

ABSTRACT

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

PMID:37920929 | DOI:10.1002/ddr.22125

Comput Struct Biotechnol J. 2023 Oct 19;21:5186-5200. doi: 10.1016/j.csbj.2023.10.038. eCollection 2023.

ABSTRACT

In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.

PMID:37920815 | PMC:PMC10618120 | DOI:10.1016/j.csbj.2023.10.038

Biomed Pharmacother. 2023 Oct 30;168:115446. doi: 10.1016/j.biopha.2023.115446. Online ahead of print.

ABSTRACT

Colistin (polymyxin E) is an antibiotic that is effective against multidrug-resistant gram-negative bacteria. However, the high incidence of nephrotoxicity caused by colistin limits its clinical use. To identify compounds that might ameliorate colistin-induced nephrotoxicity, we obtained 1707 compounds from the Korea Chemical Bank and used a high-content screening (HCS) imaging-based assay. In this way, we found that bimatoprost (one of prostaglandin F2α analogue) ameliorated colistin-induced nephrotoxicity. To further assess the effects of bimatoprost on colistin-induced nephrotoxicity, we used in vitro and in vivo models. In cultured human proximal tubular cells (HK-2), colistin induced dose-dependent cytotoxicity. The number of terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL)-positive cells, indicative of apoptosis, was higher in colistin-treated cells, but this effect of colistin was ameliorated by cotreatment with bimatoprost. The generation of reactive oxygen species, assessed using 2,7-dichlorodihydrofluorescein diacetate, was less marked in cells treated with both colistin and bimatoprost than in those treated with colistin alone. Female C57BL/6 mice (n = 10 per group) that were intraperitoneally injected with colistin (10 mg/kg/12 hr) for 14 days showed high blood urea nitrogen and serum creatinine concentrations that were reduced by the coadministration of bimatoprost (0.5 mg/kg/12 hr). In addition, kidney injury molecule-1 (KIM1) and Neutrophil gelatinase-associated lipocalin (NGAL) expression also reduced by bimatoprost administration. Further investigation in tubuloid and kidney organoids also showed that bimatoprost attenuated the nephrotoxicity by colistin, showing dose-dependent reducing effect of KIM1 expression. In this study, we have identified bimatoprost, prostaglandin F2α analogue as a drug that ameliorates colistin-induced nephrotoxicity.

PMID:37918255 | DOI:10.1016/j.biopha.2023.115446

Eur J Med Chem. 2023 Oct 23;262:115893. doi: 10.1016/j.ejmech.2023.115893. Online ahead of print.

ABSTRACT

Phosphodiesterase-4 (PDE4) and PDE5 responsible for the hydrolysis of intracellular cAMP and cGMP, respectively, are promising targets for therapeutic intervention in a wide variety of diseases. Here, we report the discovery of novel, drug-like PDE4 inhibitors by performing a high-throughput drug repurposing screening of 2560 approved drugs and drug candidates in clinical trial studies. It allowed us to identify eight potent PDE4 inhibitors with IC50 values ranging from 0.41 to 2.46 μM. Crystal structures of PDE4 in complex with four compounds, namely ethaverine hydrochloride (EH), benzbromarone (BBR), CX-4945, and CVT-313, were further solved to elucidate molecular mechanisms of action of these new inhibitors, providing a solid foundation for optimizing the inhibitors to improve their potency as well as selectivity. Unexpectedly, selectivity profiling of other PDE subfamilies followed by crystal structure determination revealed that CVT-313 was also a potent PDE5 inhibitor with a binding mode similar to that of tadalafil, a marketed PDE5 inhibitor, but distinctively different from the binding mode of CVT-313 with PDE4. Structure-guided modification of CVT-313 led to the discovery of a new inhibitor, compound 2, with significantly improved inhibitory activity as well as selectivity towards PDE5 over PDE4. Together, these results highlight the utility of the drug repurposing in combination with structure-based drug design in identifying novel inhibitors of PDE4 and PDE5, which provides a prime example for efficient discovery of drug-like hits towards a given target protein.

PMID:37918035 | DOI:10.1016/j.ejmech.2023.115893

AMB Express. 2023 Nov 2;13(1):122. doi: 10.1186/s13568-023-01634-8.

ABSTRACT

Methicillin-resistant Staphylococcus aureus (MRSA) infection poses a severe threat to global public health due to its high mortality. Currently, polymyxins are mainly used for the treatment of Gram-negative bacterial-related infection, while exhibiting limited antibacterial activities against Staphylococcus aureus (S. aureus). However, the combination of antibiotics with antibiotic adjuvants is a feasible strategy for the hard-treated infection and toxicity reducing. We will investigate the antibacterial activity of simeprevir (SIM), which treated for genotype 1 and 4 chronic hepatitis C, combined with polymyxins against MRSA through high-throughput screening technology. In our study, the synergistic antibacterial effect of SIM and polymyxins against S. aureus in vitro was found by checkerboard assay and time-growth curve. The cytotoxicity of SIM combined with polymyxin B sulfate [PB(S)] or polymyxin E (PE) in vitro was evaluated using CCK-8, human RBC hemolysis and scratch assays. In addition, we investigated the eradication of biofilm formation of S. aureus by biofilm inhibition assay and the killing of persister cells. Moreover, we evaluated the therapeutic effect and in vivo toxicity of the combination against MRSA in murine subcutaneous abscess model. Furthermore, it was preliminarily found that SIM significantly enhanced the destruction of MRSA membrane by SYTOX Green and DISC3(5) probes. In summary, these results reveal that the therapy of SIM combined with polymyxins (especially PE) is promising for the treatment of MRSA infection.

PMID:37917339 | DOI:10.1186/s13568-023-01634-8

Antioxid Redox Signal. 2023 Nov 2. doi: 10.1089/ars.2023.0282. Online ahead of print.

ABSTRACT

SIGNIFICANCE: Radiotherapy, which employs ionizing radiation to destroy or prevent the multiplication of tumor cells, has been increasingly used in the treatment of neoplastic diseases, especially cancers. However, radiation collaterally leads to prolonged periods of sperm count suppression, presumably due to impaired spermatogenesis by depleting the germ cell pool, which has long-term side effects for male reproduction.

RECENT ADVANCES: Studies of antioxidant compounds as a potential strategy for male fertility preservation have been performed mainly from animal models, aiming to prevent and restore the male germinal tissue and its function, particularly against the oxidative stress effects of radiation. Evidence in preclinical and clinical trials has shown that inhibitors of the renin-angiotensin system (RAS) and other drugs, such as statins and metformin, are candidates for ameliorating radiation-induced damage to several tissues, including the testis and prostate.

CRITICAL ISSUES: Research for developing an ideal radioprotective agent is challenging due to toxicity in the normal tissue, tumor radioresistance, response cellular to radiation, costs, regulation and timeline development. Moreover, male radioprotection experiments in humans, mainly clinical trials, are scarce and use few individuals. This scenario is reflected in the slow progress of innovation in the radioprotection field.

FUTURE DIRECTIONS: Expanding human studies to provide clues on the efficacy and safety of radioprotective compounds in the human reproductive system is necessary. Drug repurposing, frequently used in clinical practice, can be a way to shorten the development pipeline for innovative approaches for radioprotection or radiomitigation of the repercussions of radiotherapy in the male reproductive system.

PMID:37917108 | DOI:10.1089/ars.2023.0282

J Stud Alcohol Drugs. 2023 Oct 30. doi: 10.15288/jsad.23-00318. Online ahead of print.

ABSTRACT

OBJECTIVE: The surge in popularity of semaglutide (Ozempic©, Wegovy©, Rybelsus©) and other GLP-1 receptor agonists has been accompanied by widespread reports of unintended reductions in alcohol use (and other addictive behaviors) during treatment. With clinical trials of GLP-1 receptor agonists for substance use only recently underway, anecdotal reports (including via social media) are now a primary reason for interest in potential effects of GLP-1 receptor agonists on alcohol use in patient populations. The nature and volume of these reports raises the prospect that social media data can potentially be leveraged to inform the study of novel addiction treatments and the prioritization of behavioral or neurobiological targets for mechanistic research. This approach, which aligns with recent efforts to apply social media data to pharmacovigilance, may be particularly relevant for drug repurposing efforts. This possibility is illustrated by a thematic analysis of anonymous online reports concerning changes in alcohol use or alcohol-related effects during treatment with GLP-1 receptor agonists. These reports not only support the rationale for clinical trials, but point to potential neurobehavioral mechanisms (e.g., satiety, craving/preoccupation, aversion, altered subjective response) that might inform hypotheses for human laboratory and neuroscience studies.

CONCLUSIONS: Refined methods for capturing patient reports of incidental medication effects on addictive behaviors at large scale could potentially lead to novel, pharmacovigilance-based approaches to identify candidate therapies for drug repurposing efforts.

PMID:37917019 | DOI:10.15288/jsad.23-00318

BMC Bioinformatics. 2023 Nov 1;24(1):412. doi: 10.1186/s12859-023-05539-y.

ABSTRACT

BACKGROUND: The PubMed archive contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: (1) they identify a relationship but not the type of relationship, (2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, (3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or (4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues.

RESULTS: We demonstrate SKiM's ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface ( https://skim.morgridge.org ) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches.

CONCLUSIONS: SKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

PMID:37915001 | DOI:10.1186/s12859-023-05539-y